Exoity

Python based trading strategy available for MetaTrader 5

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Strategy

The strategy used for this specific algorithm is quite simple to understand. After you specify a symbol and a timeframe the script will now determine if the current candle being created is on that specific timeframe is bearish or bullish . Whenever there is a bearish candle it will sell and whenever a candle is bullish it will buy. The position will be held until stop-loss or takep-rofit values are hit or if there is a new candle. The script will continue performing this procedure infinitely.

If some of the terms used previously were unclear, the following information might be useful

Candlestick charts	Understanding Candlestick Charts
Bullish candle	Candle where close is higher than the open
Bearish candle	Candle where close is lower than the open
Stop-loss	Price where open position will close in loss
Take-profit	Price where open position will close in profit

Getting started

Make sure you have installed the dependencies of this project with pip install -r requirements.txt, have MetaTrader 5 running with a trading account in Windows 10 and have the ipynb file opened in Jupyter Notebook

_This bot can only be executed from Windows 10 OS

Trading conditions

The trading conditions section of the script is designed to tweak parameters and define new symbols.

Although the strategy is currently applied for synthetic indexes, any financial instrument that can be traded trough MT5 can be added to this script.

In order to add a new symbol, append the following code to the 5th input of the notebook.

Symbol_name = Symbol(name="NAME_OF_SYMBOL_IN_MT5",
                       lot_size=LOT_SIZE_TO_BE_TRADED,
                       stop_loss=STOP_LOSS_POINTS,
                       take_profit=TAKE_PROFIT_POINTS)

This is the example code on how XAUUSD is added

Gold = Symbol(name="XAUUSD",
                    lot_size=0.01,
                    stop_loss=500,
                    take_profit=300)

After the symbol is defined it has to be added to the SYMBOLS array on the 6th input of the notebook.

SYMBOLS.append(Gold)

Changing the symbol the strategy is being executed on can be done from the 8th input of the notebook.

symbol_to_trade = Volatility_10_1s
timeframe = 60
close_order_deviation = 20
current_ea_comments = "Exoity V1.3"

Volatility_10_1s is the selected symbol to run the strategy on

60 the amount of minutes it will last from one price check to another
in this specific case it will the H1 timeframe

20 is how far in points an order can differ from the actual price while
being executed

"Exoity V1.3" is the comments that will be registered on the MT5 transaction log

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Possible timeframes

Indicator	Minutes
M15	15
M30	30
H1	60

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Testing strategy on historical data

Once a new symbol is added, backtesting of the strategy can be done on this new symbol in cells 13 to 16. Which corresponds to the Testing the best symbol to run the strategy on section of the notebook.

Executing cells 13 to 16 will test the strategy on the symbols included in the SYMBOLS array with the data from the dates specified in cell 14 from the following timeframes M15, M30 and H1

Changing testing dates can be done on cell 14

date_from = dt(2019,1,1)
date_to = dt(2021,3,1)

The backtesting procedure is done by retrieving the data from the given dates for each mentioned timeframe and classifying each data point as bullish or bearish. With the previous information the script will now iterate through the whole dataset and start to count how many times the last two candles of a given iteration are of the same type (simulating a profitable trade) and how many times these two candles are different (simulating an unprofitable trade). The division between the quantity of profitable trades and the gross quantity of trades will allow the calculation of the percentage of trades would've been profitable.

In the scenario where no stop-loss and take-profit targets are placed, the win-rate statistic is meaningless as an unprofitable trade can represent a greater loss than a profitable trade.

Placing take-profit and stop-loss targets would allow us to avoid a situation where an unprofitable trade represents more loss than a profitable trade.

After performing the whole backtesting procedure information about the combination of timeframes and symbols that have a win-rate superior to 50.41% will be printed out in the following way SYMBOL at TIMEFRAME => WINRATE.

After testing tradeable synthetic indexes available at the following broker, the following results were obtained.

Symbol	Timeframe	Win-rate
Volatility 50 (1s) Index	M30	50.41 %
Volatility 10 (1s) Index	H1	50.45 %
Crash 500 Index	M15	51.02 %
Boom 1000 Index	M15	52.65 %

The previous results would indicate that historically, the Boom 100 index at the M15 timeframe would've been the most profitable symbol. Although this doesn't mean it will be the most profitable symbol in the future.

Executing the strategy

In order to execute the strategy, the last 3 cells must be executed. The operations will occur based on the parameters given at the Trading conditions section of the notebook.

Disclaimer

I am not a licensed advisor. This EA does not guarantee results of any kind and should be used with extreme caution. The content disclosed here is done so with an educational purpose and NOT as investment advice.